A switched-inductor (SL) multi-cell boost converter is analyzed in this paper for a high-voltage gain application, stepping up a dc voltage from 36 V to 380 V in the first stage of a photovoltaic (PV) conversion chain. A fast maximum power point tracker (MPPT), processing the system input voltage, is used to extract the maximum power from the PV generator regardless of atmospheric conditions. A single sliding-mode control (SMC) loop forces the PV generator voltage to follow the maximum power point (MPP) voltage provided by a Perturb and Observe (P&O) algorithm. The sliding-mode analysis uses the equivalent control approach to demonstrate that the linearized ideal sliding dynamics are unconditionally stable. Theoretical predictions are corroborated by simulations and experimental measurements of the system under step-type changes in input irradiance and output load. The MPPT performance is experimentally evaluated against two classical approaches applied to a canonical boost converter: a current-based SMC and a voltage-based PWM. Both approaches track the MPP current and voltage, respectively, as given by the P&O algorithm. The proposed system outperforms the two classical systems, showing a better tracking accuracy.

Recent years have witnessed an increasing demand for human fall detection systems. Among all existing methods, Wi-Fi-based fall detection has become one of the most promising solutions due to its pervasiveness. However, when applied to a new domain, existing Wi-Fi-based solutions suffer from severe performance degradation caused by low generalizability. In this paper, we propose XFall, a domain-adaptive fall detection system based on Wi-Fi. XFall overcomes the generalization problem from three aspects. To advance cross-environment sensing, XFall exploits an environment-independent feature called speed distribution profile, which is irrelevant to indoor layout and device deployment. To ensure sensitivity across all fall types, an attention-based encoder is designed to extract the general fall representation by associating both the spatial and temporal dimensions of the input. To train a large model with limited amounts of Wi-Fi data, we design a cross-modal learning framework, adopting a pre-trained visual model for supervision during the training process. We implement and evaluate XFall on one of the latest commercial wireless products through a year-long deployment in real-world settings. The result shows XFall achieves an overall accuracy of 96.8%, with a miss alarm rate of 3.1% and a false alarm rate of 3.3%, outperforming the state-of-the-art solutions in both in-domain and cross-domain evaluation.

Recent years have witnessed an increasing number of mobile devices, posing a more diversified demand for device localization solutions. While existing wireless localization solutions can obtain the relative locations of connected devices, they fall short in estimating the spatial relationships between devices that are not directly connected. To address this technical gap, we propose Wi-Prox, the first proximity estimation system for non-directly connected devices. Wi-Prox evaluates the spatial proximity of two devices by analyzing their received wireless signals. It integrates a novel multi-resolution spatial encoder that extracts multi-scale spatial features from complex-valued wireless signals, which are then analyzed and transformed into a domain-adaptive proximity metric. To enhance the general-izability of Wi-Prox, we adopt a simulation-to-reality transfer learning framework. Wi-Prox is pre-trained with a large amount of simulated data and then fine-tuned for real-world deployment, significantly reducing the need for real-world data collection. We implement Wi-Prox and evaluate its performance in both simulated and real environments. Our results indicate that a fine-tuned Wi-Prox achieves an average accuracy of 97.2% in selecting the most proximate device. Even without fine-tuning, a pre-trained Wi-Prox still manages an average accuracy of 93.8%, thereby demonstrating impressive performance in terms of both proximity estimation accuracy and domain generalizability.

The uncertain vector autoregressive model is able to model the interrelationships between different variables, which is more advantageous compared to the traditional autoregressive model, when modeling real-life objects and where the observed values are imprecise. In this paper, the parameters of the uncertain vector autoregressive model are estimated by using least absolute deviation estimation (LAD) to obtain a fitted uncertain vector autoregressive model, and residual analysis is performed to obtain estimates of expected values and variances of the residuals. In addition, future values are modeled by using forecasting methods, i.e., point estimation and interval estimation. The order of the uncertain vector autoregressive model is also determined by the indicator summation of test errors (STE) in the cross-validation, and we also analyze that the least absolute deviation estimation outperforms the least squares estimation method in the presence of outliers.

Modern home energy management systems (HEMSs) have great flexibility of energy consumption for customers, but at the same time, bear a range of problems, such as the high system complexity, uncertainty and time-varying nature of load consumptions, and renewable sources generation. This has brought great challenges for the real-time control. To solve these problems, we propose an HEMS that integrates a kernel-based real-time adaptive dynamic programming (K-RT-ADP) with a new preprocessing short-term prediction technique. For the preprocessing short-term prediction, we propose a gated recurrent unit-bidirectional encoder representations from the transformer (GRU-BERT) model to improve the forecasting accuracy of electrical loads and renewable energy generation. In particular, we classify household appliances into the temperature-sensitive loads, human activity sensitive loads, and insensitive/constant loads. The GRU-BERT model can incorporate weather and human activity information to predict load consumption and solar generation. For real-time control, we propose and employ the K-RT-ADP HEMS based on the GRU-BERT prediction algorithm. The objective of the K-RT-ADP HEMS is to minimize the electricity cost and maximize the solar energy utilization. To enhance the nonlinear approximation ability and generalization ability of the adaptive dynamic programming (ADP) algorithm, the K-RT-ADP algorithm leverages kernel mapping instead of neural networks. Hardware-in-the-loop experiments demonstrate the superiority of the proposed K-RT-ADP HEMS over the traditional ADP control through comparison.

Existing device-free localization systems have achieved centimeter-level accuracy and show their potential in a wide range of applications. However, today’s radio-based solutions fail to locate the target in millimeter-level due to their limited bandwidth and sampling rate, which constrains their applications in high-accuracy demand scenarios. We find an opportunity to break the bottleneck of existing radio-based localization systems by reconstructing the accurate signal spectral peak from the discrete samples, without changing either the bandwidth or the sampling rate of the radio hardware. This study proposes milliLoc , a millimeter-level radio-based localization system. We first derive a spectral peak reconstruction algorithm to reduce the ranging error from the previous centimeter-level to millimeter-level. Then, we improve the AoA measurement accuracy by leveraging the signal amplitude information. To ensure the practicality of milliLoc , we further extend our system to handle multi-target situations. We fully implement milliLoc on a commercial mmWave radar. Experiments show that milliLoc achieves a median ranging accuracy of 5.5 mm and decreases the AoA measurement error by 31.2% compared with the baseline. Our system fulfills the accuracy requirements of most application scenarios and can be easily integrated with other existing solutions, shedding light on high-accuracy location-based applications.

With the development of signal processing technology, the ubiquitous Wi-Fi devices open an unprecedented opportunity to solve the challenging human gesture recognition problem by learning motion representations from wireless signals. Wi-Fi-based gesture recognition systems, although yield good performance on specific data domains, are still practically difficult to be used without explicit adaptation efforts to new domains. Various pioneering approaches have been proposed to resolve this contradiction but extra training efforts are still necessary for either data collection or model re-training when new data domains appear. To advance cross-domain recognition and achieve fully zero-effort recognition, we propose Widar3.0, a Wi-Fi-based zero-effort cross-domain gesture recognition system. The key insight of Widar3.0 is to derive and extract domain-independent features of human gestures at the lower signal level, which represent unique kinetic characteristics of gestures and are irrespective of domains. On this basis, we develop a one-fits-all general model that requires only one-time training but can adapt to different data domains. Experiments on various domain factors (i.e. environments, locations, and orientations of persons) demonstrate the accuracy of 92.7% for in-domain recognition and 82.6%-92.4% for cross-domain recognition without model re-training, outperforming the state-of-the-art solutions.

Gait, the walking manner of a person, has been perceived as a physical and behavioral trait for human identification. Compared with cameras and wearable sensors, Wi-Fi-based gait recognition is more attractive because Wi-Fi infrastructure is almost available everywhere and is able to sense passively without the requirement of on-body devices. However, existing Wi-Fi sensing approaches impose strong assumptions of fixed user walking trajectories, sufficient training data, and identification of already known users. In this article, we present GaitSense , a Wi-Fi-based human identification system, to overcome the above unrealistic assumptions. To deal with various walking trajectories and speeds, GaitSense first extracts target specific features that best characterize gait patterns and applies novel normalization algorithms to eliminate gait irrelevant perturbation in signals. On this basis, GaitSense reduces the training efforts in new deployment scenarios by transfer learning and data augmentation techniques. GaitSense also enables a distinct feature of illegal user identification by anomaly detection, making the system readily available for real-world deployment. Our implementation and evaluation with commodity Wi-Fi devices demonstrate a consistent identification accuracy across various deployment scenarios with little training samples, pushing the limit of gait recognition with Wi-Fi signals.

Extensive efforts have been devoted to human gesture recognition with radio frequency (RF) signals. However, their performance degrades when applied to novel gesture classes that have never been seen in the training set. To handle unseen gestures, extra efforts are inevitable in terms of data collection and model retraining. In this article, we present XGest, a cross-label gesture recognition system that can accurately recognize gestures outside of the predefined gesture set with zero extra training effort. The key insight of XGest is to build a knowledge transfer framework between different gesture datasets. Specifically, we design a novel deep neural network to embed gestures into a high-dimensional Euclidean space. Several techniques are designed to tackle the spatial resolution limits imposed by RF hardware and the specular reflection effect of RF signals in this model. We implement XGest on a commodity mmWave device, and extensive experiments have demonstrated the significant recognition performance.

Switched inductor (SL) step-up dc-dc converters can be used for high voltage gain applications such as in PV systems. In this paper, a study of a N-cell high voltage gain boost dc-dc converter performing maximum power point tracking from a PV source is presented. First, the time domain dynamic model is derived. Then, the linearized s- domain model is first obtained. It is obtained that contrarily to the conventional canonical boost converter, the N-cell switched inductor converter presents a stable zero in the duty-cycle-to-PV-voltage transfer function which can be considered as an advantage to design a fast voltage-based MPPT control having the same response speed that corresponds to current mode control. Using the resulting control-to-output transfer function, a fast voltage-based MPPT controller is designed. Finally, numerical simulation are used to evaluate the performances of the converter when used in PV applications under different weather conditions.

Wi-Fi based sensing systems, although sound as being deployed almost everywhere there is Wi-Fi, are still practically difficult to be used without explicit adaptation efforts to new data domains. Various pioneering approaches have been proposed to resolve this contradiction by either translating features between domains or generating domain-independent features at a higher learning level. Still, extra training efforts are necessary in either data collection or model re-training when new data domains appear, limiting their practical usability. To advance cross-domain sensing and achieve fully zero-effort sensing, a domain-independent feature at the lower signal level acts as a key enabler. In this paper, we propose Widar3.0, a Wi-Fi based zero-effort cross-domain gesture recognition system. The key insight of Widar3.0 is to derive and estimate velocity profiles of gestures at the lower signal level, which represent unique kinetic characteristics of gestures and are irrespective of domains. On this basis, we develop a one-fits-all model that requires only one-time training but can adapt to different data domains. We implement this design and conduct comprehensive experiments. The evaluation results show that without re-training and across various domain factors (i.e. environments, locations and orientations of persons), Widar3.0 achieves 92.7% in-domain recognition accuracy and 82.6%-92.4% cross-domain recognition accuracy, outperforming the state-of-the-art solutions. To the best of our knowledge, Widar3.0 is the first zero-effort cross-domain gesture recognition work via Wi-Fi, a fundamental step towards ubiquitous sensing.

This paper presents Widar2.0, the first WiFi-based system that enables passive human localization and tracking using a single link on commodity off-the-shelf devices. Previous works based on either specialized or commercial hardware all require multiple links, preventing their wide adoption in scenarios like homes where typically only one single AP is installed. The key insight underlying Widar2.0 to circumvent the use of multiple links is to leverage multi-dimensional signal parameters from one single link. To this end, we build a unified model accounting for Angle-of-Arrival, Time-of-Flight, and Doppler shifts together and devise an efficient algorithm for their joint estimation. We then design a pipeline to translate the erroneous raw parameters into precise locations, which first finds parameters corresponding to the reflections of interests, then refines range estimates, and ultimately outputs target locations. Our implementation and evaluation on commodity WiFi devices demonstrate that Widar2.0 achieves better or comparable performance to state-of-the-art localization systems, which either use specialized hardwares or require 2 to 40 Wi-Fi links.

The topology of modular multilevel converters (MMC) is investigated in this paper. First, single phase half bridge inverter with split wound coupled inductors was studied, it was found that more voltage levels can be generated by using split wound coupled inductors. Therefore, new MMC topology was proposed by the analysis result, therein, two more diodes were used for each arm, and split wound coupled inductors were applied to the modular multilevel converter (MMC) to replace the current-limiting inductors. The theoretic analysis and simulation results have verified that, the number of the voltage levels of modular multilevel inverter has increased from n levels to 2n-3 levels, thus slew rate of the voltage (dv/dt) was suppressed and the total harmonic distortion (THD) was reduced.